Structured online databases continue to grow and be used today. For example, shopping or browsing items that can be purchased on the Internet is a common activity in today's society. Shopping, auction, social, and other websites provide the convenience of browsing from home or office, and can list hundreds of thousands or millions of items that may be of potential interest to a user. Websites such as eBay provide access to a large variety of consumer products that could be of interest to a shopper. These products combine to form lists which are constantly being added to and subtracted from as the merchant or third party sellers add items for sale and as other consumers purchase items, thus removing listings. Searching through all of the items can be a cumbersome task. Repeated searches for the same types of items turn up items that the consumer has seen before and decided not to buy. Other websites provide other forms of continuously updated lists of numerous items through which a user must browse to find valuable content—a similarly cumbersome task. Additional complications stem from limitations of the database searched, limitations of a user's hardware and software, and limitations of a user's ability to digest a large amount of data in a single session.
In the particular example of eBay, every evening a consumer may want to browse or search for certain types of items to purchase. eBay lists all active listings commingled with any items listed since the night before. The consumer can choose to view the auction items resulting from a search in a few ways, including by “most recently listed” or “items ending soonest” or those with a “Buy-It-Now” option. In either scenario, the consumer will usually have to waste time looking through items previously seen, or risk missing a great deal on a “Buy-It-Now” item just listed (and purchased by another consumer) while the consumer was busily scanning through the items ending soonest.
The consumer has roughly the same need to evaluate an auction item ending in ten minutes as they do a “Buy-It-Now” item that was listed ten minutes previously—each might represent a great purchase opportunity, but as each minute passes, the consumer's need to evaluate the auction item increases while the likelihood that they are missing a great deal in the “Buy-It-Now” item decreases.
If an eBay search yields forty thousand listings, the consumer might reasonably scan through them all over a period of a week or two, but performing that operation without an effective way to de-duplicate results and without risking missing new buyable listings every evening is not feasible.
U.S. Pat. No. 6,487,553, to Emens et al. and assigned to International Business Machines Corporation describes a method for reducing search results by manually or automatically excluding previously presented search results. However, this method requires that the search results be stored locally, and that the user either flags results to be excluded or flags results to be presented a second time.
U.S. Pat. No. 8,244,709, to Wen et al. and assigned to eBay, Inc., describes a method of automatically updating searches. In this method, however, the search results must be stored locally in order to be compared against each other.
There exists a need for a way to see all results without storing the data, as a consumer cannot retrieve all search results from a database and subsequently delete each result after each view. This scenario could necessitate a retrieval of an overwhelming amount of listings. There also exists a need for a way to see all items resulting from a particular search, ordered according to time sensitivity. By contrast, if the consumer has already evaluated an item and has discarded it, there needs to be a reliable method to assure the consumer doesn't see the same item in the search results again.